autogen VS EdgeChains

AutoGen is an AI framework by Microsoft designed to streamline multi-agent conversations. AutoGen allows agents to communicate, share information, and make collective decisions. This setup enhances the responsiveness and dynamism of conversations. Developers use AutoGen to tailor agents to specific roles, such as programmer, content writer, CEO, etc. This enhances their ability to handle tasks from simple queries to intricate problem-solving.

I am still playing around with the project but FYI, the parsing for the github repo URL at https://glide.agenticlabs.com/ will fail if there's a trailing slash in the repo link i.e. https://github.com/microsoft/autogen/ won't work but https://github.com/microsoft/autogen will.

New example notebook demoing video transcript translate with whisper.

New release: v0.2.1

I disagree with the conclusion. In software, I've seen 10x engineers in person and I don't think they're replaceable. Whereas, the new college grad or that entry level dev who doesn't design anything and just writes small amounts of code, doing exactly as told is replaceable by an AI. Frameworks similar to Microsoft Autogen(https://github.com/microsoft/autogen) can in theory build agents who can do these tasks with ease whereas a 10x engineer can focus on directing the agents and designing systems.

Microsoft autogen

CompressibleAgent (experimental) can be used to handle long conversations. Notebook: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb

We build a WASM compiler to compile our prompts and chains into webassembly. Honojs was a critical part of it.

https://github.com/arakoodev/EdgeChains/

EdgeChains: github.com/arakoodev/EdgeChains

would love your thoughts on this as well - https://github.com/arakoodev/edgechains

got frustrated in the same way with "Black Box Prompting - every library hides prompts/chains in layers of libraries...while it should have been declarative.

EdgeChains - allows u to specify ur prompt and chain in jsonnet. This why i think Generative AI needs declarative orchestration and not previous generations. https://github.com/arakoodev/edgechains#why-do-you-need-decl...

when will you have pgvector as a destination ? we (https://github.com/arakoodev/edgechains) work with a lot of enterprises and they would not move away from using redis or pgvector even as their vector store. Is there a way where we can leverage that ?

Second, for a LOT of enterprises, they want to use non-openai embedding models (minilm, GTE, BGE), will you support that. For e.g. in Edgechains we natively support BGE and minilm. Would you be able to support that ?

oh really ? Thats awfully kind. I'll take that in for EdgeChains as well.

https://github.com/arakoodev/EdgeChains/issues/172

Promptfile is written in markdown, which is unsuited for templates and config management.

I have an attempt in the same domain, would love feedback

We didnt invent a new markup - we used jsonnet which is used in large scale kubernetes and has a grammar that has been well tested for config mgmt.

https://github.com/arakoodev/EdgeChains/blob/main/Examples/r...

Prompts live outside the code.